Graduate Student Researcher

~~puppy~~ dog Mira.

Assessing Inference Quality for Probabilistic Programs using Multivariate Simulation Based Calibration

Sharan Yalburgi, Cameron Freer, Jameson Quinn, Veronica Weiner, **Sam Witty**, Vikash Mansinghka (2021).

Third Conference on Probabilistic Progamming. [bibtex]

Causal Probabilistic Programming without Tears

Eli Bingham*, James Koppel*, Alexander Lew*, Robert Ness*, Zenna Tavares*, **Sam Witty***, Jeremy Zucker* (2021, Alphabetical Order).

Third Conference on Probabilistic Progamming. [bibtex]

__A Simulation-Based Test of Identifiability for Bayesian Causal Inference__

**Sam Witty**, David Jensen, Vikash Mansinghka (2021).

arXiv preprint arXiv:2102.11761 [bibtex]. (Working paper)

__Fairkit, Fairkit, on the Wall, Who’s the Fairest of Them All? Supporting Data Scientists in Training Fair Models__

Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, **Sam Witty**, Stephen Giguere, Yuriy Brun (2020).

arXiv preprint arXiv:2012.09951 [bibtex]

__Causal Inference using Gaussian Processes with Structured Latent Confounders__

**Sam Witty**, Kenta Takatsu, David Jensen, Vikash Mansinghka (2020).

International Conference on Machine Learning. [bibtex]

__Bayesian Causal Inference via Probabilistic Program Synthesis__

**Sam Witty***, Alexander Lew*, David Jensen, Vikash Mansinghka (2020).

Second Conference on Probabilistic Programming [bibtex]

__Measuring and Characterizing Generalization in Deep Reinforcement Learning__

**Sam Witty**, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen (2018).

arXiv preprint arXiv:1812.02868 [bibtex]

(Short Version Published at the NeurIPS CRACT Workshop.)

__Causal Graphs vs. Causal Programs: The Case of Conditional Branching.__

**Sam Witty**, David Jensen (2018).

First Conference on Probabilistic Programming. [bibtex]

__Belief-Space Planning for Automated Malware Defense.__

Justin Svegliato, **Sam Witty**, Amir Houmansadr, Shlomo Zilberstein (2018).

IJCAI Workshop on AI for Internet of Things. [bibtex]

- I presented our
__poster__on "Bayesian Causal Inference via Probabilistic Program Synthesis" at the__Second Conference on Probabilistic Programming.__(October 23, 2020) - I gave a
__talk__on "Causal Inference using Gaussian Processes with Structured Latent Confounders" at the__International Conference on Machine Learning.__(July 16, 2020) - I presented our
__poster__on "Generalization in Deep Reinforcement Learning" at the__Microsoft Research New England Machine Learning Day.__(May 10, 2019) - I presented our
__poster__on "Generalization in Deep Reinforcement Learning" at the__NeurIPS Workshop on Critiquing and Correcting Trends in Machine Learning.__(December 7, 2018) - I presented our
__poster__on "Causal Graphs vs. Causal Models: The Case of Conditional Branching" at the__International Conference on Probabilistic Programming.__(October 5, 2018) - I gave a tutorial on “Causal Inference with Graphical Models” for the
__UMass Graduate Researchers in Data.__(November 29, 2017). - I gave an invited talk on "Computational Representations of Causality" for the
__MIT Probabilistic Computing Group.__(November 2, 2017)

- I was the teaching assistant for
__CS348__, Umass' upper-level undergraduate course on data science. (Spring, 2019) - I gave a guest lecture on deep learning for CS589, Umass' Masters course on machine learning. (February 15, 2018)
- I mentored Catherine Chen, a visiting undergraduate researcher sponsored by the NSF's REU program. (Summer, 2017)